The think-tankers on the Executive Leadership Council at AIIM systematically use a four-box matrix to reduce uncertainty, allocate investments and calibrate new product/service initiatives. This simple tool -- with "important and difficult" in the upper right and "unimportant and easy" in the lower left -- produces surprisingly powerful insights.

During year-end discussions with 40 executives in 20 vertical markets, I discovered that they all now place big data in that upper-right quadrant. Similarly, readers of Booz & Co.'s Strategy+Business blog designated big data the 2013 Strategy of the Year, and the co-directors of Cognizant's Center for the Future of Work, in a masterful white paper, placed big-data-enabled "meaning making" at the pinnacle of strategic endeavor.

That was enough to prompt me to roll up my sleeves and systematically examine, vertical market by vertical market, how organizations are organizing their path to big data mastery.

Over the past few months, I embarked on a study of how North America's 7,100 banks are approaching the big data opportunity. I found that 20% are doing nothing, 25% are preparing to do something, 30% are currently doing something and 25% are achieving mastery.

Recognizing that each bank is unique, with its own capabilities and habits and its own market reality, I nevertheless sought to identify patterns of behavior evidenced by those banks that seemed to have achieved mastery in big data. The following 10-step, high-level pattern repeated itself in institutions possessing differentiated big data capabilities:

* Step 1: Decide to do something.

* Step 2: Craft a narrative.

* Step 3: Access Type 1 smartness.

* Step 4: Inventory analytical resources.

* Step 5: Assess readiness.

* Step 6: Centrally manage analytical resources.

* Step 7: Create analytic capability.

* Step 8: Obtain the support of senior management.

* Step 9: Act on insight.

* Step 10: Link to behavior.

Type 1 smartness, by the way, is the sort of intelligence possessed by someone who can do unstructured problem-solving, like a doctor, who can diagnose a situation and propose an appropriate course of action.

While the path-to-mastery pattern is conceptually simple, successfully executing it requires courage, perseverance and patience. Delivering the true value of big data is important and difficult.

The thing they don't tell you is that it takes time. Acquiring the body of knowledge, learning the language, adopting the ideas and making the cultural adjustments required for harnessing full value from big data is a cumulative process. The path to big data mastery took one entertainment conglomerate seven years -- three to decide to do something and four to build out the infrastructure. Chris Wegrzyn, director of data architecture for the Democratic National Committee, explained to The Huffington Post why the ramp-up to big data mastery took two years for the Obama 2012 campaign: "It's one thing to build up some technology and hire some people. It's another thing entirely to transform how your operation works fundamentally."